Seattle’s Cycle Sharing Analysis

We find the orignal information here.

1 Introduction

This report and analysis on Seattle’s bicycle sharing trends. The data includes weather reports for the area, the station locations, and trips taken by cycle riders. Future explorations could include the addition of other forms of public transportation for the Seatle area to better understand commuter travel trends.

2 Station Locations

Lets take a look at where the rental stations are located in Seattle! See Figure 2.1

Figure 2.1: Station Locations

It looks like all those locations are pretty centralized. Lets take a closer look by zooming in in Figure 2.2 2.2

Figure 2.2: Stations map Zoomed In

Look at all those stations! Looks like plenty of places to pick up a bicycle! Lets take a closer look at the some of the station’s statistics.

3 Histogram of Number of Bikes per Station

Figure 3.1: Current Dock Count

Here is a histogram of the number of bikes per station. Some stations definitely have more bikes than others. Lets take a look at how some of these stations have had to expand to accomodate increased traffic. No one wants to show up at a destination where they can’t leave they’re bike!

Figure 3.2: Change in Number of Bike Docks Per Station

Not many of those kept the same number but lets see how many times each station had a change in its number of stations.

Figure 3.3: Current Station Size

It looks like 11 stations lost bike docks, 39 docks stayed the same, and 8 stations gained docks. Now that we have our stations and docks worked out, lets take a look at the trip data.

4 Trips

Let’s start by looking at or number of trips per day.

4.1 Number of Trips per Day

Figure 4.1: Time to visualize the number of rides per day

We can definitely see a drop during the colder winter months and a steady rise during the spring. It’s curious to think on that day in March when they had a sudden drop in trips. Maybe it was a particularly cold day. Lets move on and see how the number of trips changes by month with Figure 4.2

4.2 Plotting trips per month (by season, minus summer)

Figure 4.2: Trips Per Month (By Season)

Wow that isn’t what I would expect. We can see a clear rise in trips during the month of March. Not entirely sure what is happening there. # Trip Durations Now that we have an idea of how many trips there were during each month, lets take a look at how those trips break down. ## Average Trip Duration by Date Lets take a look at how the month affects the average trip duration.

Figure 4.3: Average Trip Duration

As we can see in Figure ??, the average trip duration was pretty consistant between the months with the exception of January which saw a noticably smaller trip duration which were consistantly smaller.

Next lets look at how trips break down by day of the week . # Day of the Week Lets see how the day of the week affects the trips. We’ll begin with looking at comparison of the number of trips by day of the week. ##Number of Trips by Day of Week We can see from this graph that the summer saw the most trips and there was a relatively larger number of trips on Thursday. ##Number of Trips Per Time of Day

from this graph we can get some insite into what people are traveling for. On Saturday and Sunday we see a most of our trips taking place in the afternoon. During Weekdays, we see more trips occuring during early morning and later afternoon. Perhaps these bikes are being used for commuting to work on weekdays. # Member Type This bicycle sharing company has two types of bike rentals which effect the price. Members recieve a cheaper ride while being a short term pass holder costs a little more. Lets see how being a member effects the ammount of rides people take. ##Number of Trips by Member Type With this graph we can see a higher number of members using the bikes over short term pass holders. ##Trip Duration by Member Type It seems that members also have a more consistant spread of trip durations compaired to short term pass holders. ##Member Demographics Lets take a look at the spread of members by age to get a better idea of the people who ride the shared bikes are. It looks like most of our members are in their late twenties to early thirties.

5 Fees

Lets take a look at the amount of fees each user type racks up. Since members don’t get charged for trips less than 45 minutes, they should have fewer fees.

Figure 5.1: Fees By Member Typer

In general we see that members do recieve fewer fees overall and typically don’t have many expensive ones.

6 Weather

One of the big factors as to whether or not people want to go out and ride bicycles is the weather. Lets start off by looking at the temperature ## Daily Minimum Temperature

## [1] "2016-02-14"

(#fig:temps min)Minimum Daily Temp

6.1 Mean Temperature

(#fig:temp means)Mean Daily Temp

6.2 Max Temperature

(#fig:temps max)Max Daily Temp

Now that we have an idea for our temperature by day, lets get a look at our weather patterns. Rain would be a good reason for someone to not want to go out and ride bicycles.

6.3 Events

## 'data.frame':    689 obs. of  21 variables:
##  $ Date                      : Date, format: "2014-10-13" "2014-10-14" ...
##  $ Max_Temperature_F         : int  71 63 62 71 64 68 73 66 64 60 ...
##  $ Mean_Temperature_F        : num  62 59 58 61 60 64 64 60 58 58 ...
##  $ Min_TemperatureF          : int  54 55 54 52 57 59 55 55 55 57 ...
##  $ Max_Dew_Point_F           : int  55 52 53 49 55 59 57 57 52 55 ...
##  $ MeanDew_Point_F           : int  51 51 50 46 51 57 55 54 49 53 ...
##  $ Min_Dewpoint_F            : int  46 50 46 42 41 55 53 50 46 48 ...
##  $ Max_Humidity              : int  87 88 87 83 87 90 94 90 87 88 ...
##  $ Mean_Humidity             : int  68 78 77 61 72 83 74 78 70 81 ...
##  $ Min_Humidity              : int  46 63 67 36 46 68 52 67 58 67 ...
##  $ Max_Sea_Level_Pressure_In : num  30 29.8 30 30 29.8 ...
##  $ Mean_Sea_Level_Pressure_In: num  29.8 29.8 29.7 29.9 29.8 ...
##  $ Min_Sea_Level_Pressure_In : num  29.6 29.5 29.5 29.8 29.7 ...
##  $ Max_Visibility_Miles      : int  10 10 10 10 10 10 10 10 10 10 ...
##  $ Mean_Visibility_Miles     : int  10 9 9 10 10 8 10 10 10 6 ...
##  $ Min_Visibility_Miles      : int  4 3 3 10 6 2 6 5 6 2 ...
##  $ Max_Wind_Speed_MPH        : int  13 10 18 9 8 10 10 12 15 14 ...
##  $ Mean_Wind_Speed_MPH       : int  4 5 7 4 3 4 3 5 8 8 ...
##  $ Max_Gust_Speed_MPH        : num  21 17 25 0 0 0 18 0 21 22 ...
##  $ Precipitation_In          : num  0 0.11 0.45 0 0.14 0.31 0 0.44 0.1 1.43 ...
##  $ Events                    : Factor w/ 7 levels "Fog","Fog-Rain",..: 4 4 4 4 4 4 3 4 4 4 ...

That is definitely a lot of rain and other events. Next lets look the number of trips and the temperature.

6.4 Mean Temperature vs. Number of Trips

It looks like most trips occur when its around 50 degrees outside. Next lets try to normalize the data to see how temperature really affects the number of trips. ## Normalize In this graph we see that there are a lot more trips on days when it’s warmer outside. Colder days saw a fewer number of days. ##Precipitation vs. Number of Trips Finally lets take a look at the number of trips and how rain effects that. Not suprisingly, there are fewer trips on days where there is more rain.